Optical coherence tomography (OCT) has become a valuable clinical imaging tool, since its introduction in 1991. OCT is based on an optical measurement technique known as low-coherence interferometry. OCT performs high resolution, cross-sectional imaging of internal microstructure of a physical object by directing a light beam to the physical object, and then measuring and analyzing magnitude and time delay of backscattered light. A cross-sectional image is generated by performing multiple axial measurements of time delay (axial scans or A-scans) and scanning the incident optical beam transversely. This produces a two-dimensional data set of A-scans (i.e. B-scans), which represents the optical backscattering in a cross-sectional plane through the physical object. Three-dimensional, volumetric data sets can be generated by acquiring sequential cross-sectional images by scanning the incident optical beam in a raster pattern (three-dimensional OCT (3D-OCT)). This technique yields internal microstructural images of the physical objects with very fine details. For example, pathology of a tissue can effectively be imaged in situ and in real time on the micron scale.
Several types of OCT systems and methods have been developed, for example, time-domain OCT (TD-OCT) and Fourier-domain OCT (FD-OCT). Use of FD-OCT enables high-resolution imaging of retinal morphology that is nearly comparable to histologic analysis. Examples of FD-OCT technologies include spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT).
OCT may be used for identification of common retinovascular diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinovascular occlusions.
However, despite the rapid evolution of OCT imaging, current OCT technology may not provide adequate visualization of retinal and choroidal microvasculature. Thus, clinicians are often compelled to order both OCT and fluorescein angiography (FA) in patients with retinovascular diseases. There has been an increased interest in using data generated during FD-OCT imaging to generate angiographic images of the fundus (OCT Angiography).
A variety of OCT Angiography techniques have been developed including but not limited to optical microangiography (OMAG), speckle variance, phase variance, correlation mapping, and decorrelation (see for example, US Patent Publication No. 2008/0025570; US Patent Publication No. 2010/0027857; US Patent Publication No. 2012/0307014; Fingler et al. “Mobility and transverse flow visualization using phase variance contrast with spectral domain optical coherence tomography” Opt. Express 2007, 15:12636-53; Mariampillai et al., “Speckle variance detection of microvasculature using swept-source optical coherence tomography”, Optics Letters 33(13), 1530-1533, 2008; An et al., “In vivo volumetric imaging of vascular perfusion within human retina and choroids with optical micro-angiography,” Opt. Express 16(15), 11438-11452, 2008; Enfield et al., “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography” (cmOCT), Biomed. Opt. Express 2(5), 1184-1193, 2011; and Jia et al. “Split-spectrum amplitude decorrelation angiography with optical coherence tomography” Optics Express 20(4) 4710-4725 (2012), the contents of all of which are hereby incorporated by reference). These techniques use the OCT data to achieve the imaging of functional vascular networks within microcirculatory tissue beds in vivo, without the use of exogenous contrast agents.
The key point of OCT angiography processing methods is to extract localized signal variations from the bulk motion signal of a background tissue by comparing OCT signals, such as B-scans, captured at different time points in the course of a single examination of an eye. Processing can be carried out on the complex OCT data (complex-based), the amplitude or intensity portion of the OCT data (intensity-based), or the phase portion of the data (phase-based). The separately processed intensity and phase information can also be combined in some approaches. Intensity-based approaches such as speckle variance, correlation mapping, and decorrelation, are easier to implement because of the reduced requirements for motion compensation and processing. They are also less sensitive to phase noise. However, intensity-based approaches have limitations in visualizing slower flows. Since the phase signal is more sensitive to the motion signal, phase-based approaches, such as phase variance, have much higher sensitivity to flow compared to the intensity-based approaches. But, approaches using only phase information can have increased error in measurements at low backscattered signal intensity. And, due to the higher flow sensitivity, phase-based OCT angiography methods also are able to detect very small motions in adjacent non-vascular tissue, for example, in the retinal nerve fiber layer. These motions commonly appear as artifacts in OCT angiograms.
It can therefore be advantageous to include both the amplitude and phase information in the OCT Angiography processing as in complex-based approaches. The biggest drawback to these types of approaches is the heavy computational load. With advances in parallel processing such as graphic processing units (GPUs), FPGAs, etc, the processing times required for complex optical coherence tomography angiography techniques becomes manageable. Several groups have proposed complex-based angiography processing techniques (see for example, US Patent Publication No. 2012/0277579 and US Patent Publication No. 2012/0307014, the contents of which are hereby incorporated by reference). Here we propose several additional complex-based OCT angiography processing approaches that in the past have only been applied to intensity data.
As mentioned, phase-based approaches can yield images with increased artifacts given their higher flow sensitivity. While tissue motion artifacts are not necessarily difficult to detect visually, they can limit the usefulness of the resulting images. Here we propose methods for removing artifacts through the use of two or more image analysis methods or imaging methods whose sensitivities to the artifact type in question differ.
Glaucoma refers to a group of eye diseases in which the optic nerve degenerates. Due to the degeneration of optic nerve fibers, glaucoma causes progressive, irreversible death of retinal ganglion cells, eventually leading to blindness. As the second leading cause of blindness, a number of parameters have been studied as possible causal factors in glaucoma (see for example, An, Lin, et al. “Noninvasive imaging of pulsatile movements of the optic nerve head in normal human subjects using phase-sensitive spectral domain optical coherence tomography.” Optics letters 38.9 (2013): 1512-1514). Parameters include elevated intraocular pressure (IOP), low ocular perfusion pressure, increased scleral elasticity, age, ethnicity, myopia, local vascular abnormalities, and alterations in biomechanical properties of the optic nerve head (ONH) among others (see for example, Chang, Elma E., and Jeffrey L. Goldberg. “Glaucoma 2.0: neuroprotection, neuroregeneration, neuroenhancement.” Ophthalmology 119.5 (2012): 979-986). However, the mechanism by which retinal ganglion cells are damaged in glaucoma remains controversial, presumably because individual patients demonstrate a wide range of sensitivities to these risk factors. For example, although elevated IOP has been considered as the primary risk factor for the development of glaucoma, it still cannot be used as a reliable indicator either of the glaucomatous status or likelihood of progressive ONH changes. Both ocular hypertensives (i.e., people with high IOP in the absence of glaucoma) and normal or low tension glaucoma (i.e., glaucoma with normal or low IOP) are common.
Evidence increasingly suggests that abnormal biomechanical properties of the ONH may play an important role in the development of glaucoma (see for example, Leske, M. Cristina. “Open-angle glaucoma an epidemiologic overview.” Ophthalmic epidemiology 14.4 (2007): 166-172). Ganglion cell axons form into bundles and pass through pores in the lamina cribrosa of the ONH before exiting the eye. Pulsatile changes in IOP caused by blood flow might lead to pulsatile deformation of the tissues through which the blood flows. In the presence of abnormal ONH biomechanical properties, stress and strain resulting from pulse-induced motion could directly damage the retinal ganglion cells (see for example, Sigal, Ian A., and C. Ross Ethier. “Biomechanics of the optic nerve head.” Experimental eye research 88.4 (2009): 799-807), disturb the capillary circulation perfusion of the retina nerve fiber layer (RNFL), or obstruct nutrient transport to (see for example, Quigley, Harry A., et al. “The mechanism of optic nerve damage in experimental acute intraocular pressure elevation.” Investigative ophthalmology & visual science 19.5 (1980): 505-517) or cause chronic progressive deformation of ONH structures. In particular, the large arteries near the optic nerve head may deform the nerve head itself as well as the peripapillary RNFL tissue through which the arteries move to access the rest of the eye.
Characterization of pulse-induced axial RNFL movement in vivo would provide a valuable tool to evaluate RNFL biomechanical properties because such properties determine RNFL responses to IOP forces impinging on it. The extent to which ONH mechanical properties determine susceptibility to damage from IOP is unknown because functional measurement tools have been lacking. Pulse-induced movement of the RNFL offers one such tool, but no currently available technology is capable of measuring RNFL movement, probably because it is too small (typically a few micrometers).
Intraretinal or under retinal fluid spaces, have been detected in a plurality of retinal diseases, such as diabetic retinopathy, exudative age-related macular degeneration, or retinitis pigmentosa. Images of these spaces, captured by optical coherence tomography (OCT) have been reported in several research papers (see for example, Fung et al. “An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (Lucentis) for neovascular age-related macular degeneration” Am J Ophthalmol. 2007, 143(4)566-583; Massin et al., Optical coherence tomography for evaluating diabetic macular edema before and after vitrectomy. Am J Ophthalmol. 2003, 135(2)169-177; Melo et al. “Intravitreal injection of bevacizumab for cystoid macular edema in retinitis pigmentosa” Acta Ophthalmol Scand. 2007, 85(4)461-463; and Gaudric et al. “Optical coherence tomography in group 2A idiopathic juxtafoveolar retinal telangiectasis” Arch Ophthalmol. 2006, 124(10)1410-1419, the contents of all of which are hereby incorporated by reference). The exact content of the spaces is unknown, but clinicians and scientists have attempted to investigate these spaces through the exhibited different optical properties made visible with OCT, which may help them to evaluate retinal diseases more thoroughly. For example, retreatment with ranibizumab for age-related exudative macular degeneration is dependent on whether the cysts are exudative or degenerative.
The motion properties of the fluid filled spaces have not been investigated yet due to the lack of appropriate examination tools. The motion properties may indicate whether the retinal fluid is active or not, helping the clinicians to make a correct diagnosis and establish better treatment strategies. Here we propose a technique that takes advantage of the different sensitivities of different motion contrast processing approaches to obtain more information on intraretinal fluid filled spaces.